Overview

Brought to you by YData

Dataset statistics

Number of variables32
Number of observations110536
Missing cells0
Missing cells (%)0.0%
Duplicate rows8223
Duplicate rows (%)7.4%
Total size in memory27.8 MiB
Average record size in memory264.0 B

Variable types

Numeric8
Categorical24

Alerts

Dataset has 8223 (7.4%) duplicate rowsDuplicates
Age is highly overall correlated with Is_Not_In_School and 2 other fieldsHigh correlation
Class_Of_Worker is highly overall correlated with Is_Not_WorkingHigh correlation
Education_Primary is highly overall correlated with Education_SecondaryHigh correlation
Education_Secondary is highly overall correlated with Education_PrimaryHigh correlation
Employment_Status_NotInWorkForce is highly overall correlated with Is_Not_WorkingHigh correlation
Employment_Status_Unemployed is highly overall correlated with Has_Not_Looked_For_Work_WeekHigh correlation
Has_Not_Looked_For_Work is highly overall correlated with Has_Not_Wanted_More_HoursHigh correlation
Has_Not_Looked_For_Work_Week is highly overall correlated with Employment_Status_UnemployedHigh correlation
Has_Not_Wanted_More_Hours is highly overall correlated with Has_Not_Looked_For_WorkHigh correlation
Hours_Work_Total is highly overall correlated with Is_Not_Working and 1 other fieldsHigh correlation
Is_Not_In_School is highly overall correlated with AgeHigh correlation
Is_Not_Working is highly overall correlated with Class_Of_Worker and 3 other fieldsHigh correlation
Marital_Status is highly overall correlated with Age and 1 other fieldsHigh correlation
NatureOfEmployment_Permanent is highly overall correlated with NatureOfEmployment_Short-TermHigh correlation
NatureOfEmployment_Short-Term is highly overall correlated with NatureOfEmployment_PermanentHigh correlation
Relationship is highly overall correlated with Age and 1 other fieldsHigh correlation
Work48H_Requirement is highly overall correlated with Work48H_WantHigh correlation
Work48H_Want is highly overall correlated with Work48H_RequirementHigh correlation
Working_Hours_Per_Week is highly overall correlated with Hours_Work_Total and 1 other fieldsHigh correlation
Is_Not_In_Techvoc is highly imbalanced (70.3%) Imbalance
Has_Not_Looked_For_Work is highly imbalanced (64.3%) Imbalance
Is_Not_First_Time is highly imbalanced (89.7%) Imbalance
No_Other_Job is highly imbalanced (69.8%) Imbalance
Has_Not_Looked_For_Work_Week is highly imbalanced (86.1%) Imbalance
Education_Business is highly imbalanced (73.9%) Imbalance
Education_Other is highly imbalanced (68.0%) Imbalance
Education_STEM is highly imbalanced (61.3%) Imbalance
Work48H_Other is highly imbalanced (99.7%) Imbalance
Work48H_Passion is highly imbalanced (98.4%) Imbalance
Work48H_Requirement is highly imbalanced (68.4%) Imbalance
Work48H_Want is highly imbalanced (67.4%) Imbalance
Employment_Status_Unemployed is highly imbalanced (77.4%) Imbalance
Marital_Status has 37943 (34.3%) zeros Zeros
Class_Of_Worker has 3296 (3.0%) zeros Zeros

Reproduction

Analysis started2025-03-27 19:46:32.491803
Analysis finished2025-03-27 19:46:46.525708
Duration14.03 seconds
Software versionydata-profiling vv4.15.1
Download configurationconfig.json

Variables

Household_Size
Real number (ℝ)

Distinct20
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.403778
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:46.584698image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median5
Q37
95-th percentile10
Maximum23
Range22
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.5907033
Coefficient of variation (CV)0.47942446
Kurtosis1.8015737
Mean5.403778
Median Absolute Deviation (MAD)2
Skewness1.0045479
Sum597312
Variance6.7117436
MonotonicityNot monotonic
2025-03-28T03:46:46.683957image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4 19686
17.8%
5 18508
16.7%
6 14981
13.6%
3 14685
13.3%
7 11246
10.2%
2 8378
7.6%
8 7717
 
7.0%
9 4778
 
4.3%
10 3130
 
2.8%
1 2819
 
2.6%
Other values (10) 4608
 
4.2%
ValueCountFrequency (%)
1 2819
 
2.6%
2 8378
7.6%
3 14685
13.3%
4 19686
17.8%
5 18508
16.7%
6 14981
13.6%
7 11246
10.2%
8 7717
 
7.0%
9 4778
 
4.3%
10 3130
 
2.8%
ValueCountFrequency (%)
23 23
 
< 0.1%
19 19
 
< 0.1%
18 48
 
< 0.1%
17 111
 
0.1%
16 91
 
0.1%
15 331
 
0.3%
14 371
 
0.3%
13 836
0.8%
12 1070
1.0%
11 1708
1.5%

Relationship
Real number (ℝ)

High correlation 

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6701527
Minimum1
Maximum11
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:46.776090image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile8
Maximum11
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.0608067
Coefficient of variation (CV)0.77179357
Kurtosis3.1550992
Mean2.6701527
Median Absolute Deviation (MAD)1
Skewness1.8000234
Sum295148
Variance4.2469242
MonotonicityNot monotonic
2025-03-28T03:46:46.867913image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 39605
35.8%
3 30487
27.6%
2 22538
20.4%
8 5070
 
4.6%
6 3902
 
3.5%
5 3421
 
3.1%
4 2239
 
2.0%
7 1560
 
1.4%
11 948
 
0.9%
10 731
 
0.7%
ValueCountFrequency (%)
1 39605
35.8%
2 22538
20.4%
3 30487
27.6%
4 2239
 
2.0%
5 3421
 
3.1%
6 3902
 
3.5%
7 1560
 
1.4%
8 5070
 
4.6%
9 35
 
< 0.1%
10 731
 
0.7%
ValueCountFrequency (%)
11 948
 
0.9%
10 731
 
0.7%
9 35
 
< 0.1%
8 5070
 
4.6%
7 1560
 
1.4%
6 3902
 
3.5%
5 3421
 
3.1%
4 2239
 
2.0%
3 30487
27.6%
2 22538
20.4%

Age
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.445828
Minimum0
Maximum99
Zeros148
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:46.978652image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q124
median35
Q350
95-th percentile69
Maximum99
Range99
Interquartile range (IQR)26

Descriptive statistics

Standard deviation17.605283
Coefficient of variation (CV)0.47015339
Kurtosis-0.32611659
Mean37.445828
Median Absolute Deviation (MAD)13
Skewness0.46615142
Sum4139112
Variance309.94599
MonotonicityNot monotonic
2025-03-28T03:46:47.103229image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22 2812
 
2.5%
23 2805
 
2.5%
25 2711
 
2.5%
24 2673
 
2.4%
21 2646
 
2.4%
20 2604
 
2.4%
28 2559
 
2.3%
26 2543
 
2.3%
30 2447
 
2.2%
35 2405
 
2.2%
Other values (90) 84331
76.3%
ValueCountFrequency (%)
0 148
 
0.1%
1 144
 
0.1%
2 160
 
0.1%
3 160
 
0.1%
4 166
 
0.2%
5 388
0.4%
6 444
0.4%
7 506
0.5%
8 549
0.5%
9 589
0.5%
ValueCountFrequency (%)
99 8
 
< 0.1%
98 14
 
< 0.1%
97 9
 
< 0.1%
96 11
 
< 0.1%
95 19
 
< 0.1%
94 13
 
< 0.1%
93 20
< 0.1%
92 34
< 0.1%
91 26
< 0.1%
90 49
< 0.1%

Is_Rural
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
61585 
0.0
48951 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 61585
55.7%
0.0 48951
44.3%

Length

2025-03-28T03:46:47.214187image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:47.298411image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 61585
55.7%
0.0 48951
44.3%

Most occurring characters

ValueCountFrequency (%)
0 159487
48.1%
. 110536
33.3%
1 61585
 
18.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 159487
48.1%
. 110536
33.3%
1 61585
 
18.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 159487
48.1%
. 110536
33.3%
1 61585
 
18.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 159487
48.1%
. 110536
33.3%
1 61585
 
18.6%

Is_Female
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
59455 
1.0
51081 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 59455
53.8%
1.0 51081
46.2%

Length

2025-03-28T03:46:47.388193image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:47.472007image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 59455
53.8%
1.0 51081
46.2%

Most occurring characters

ValueCountFrequency (%)
0 169991
51.3%
. 110536
33.3%
1 51081
 
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 169991
51.3%
. 110536
33.3%
1 51081
 
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 169991
51.3%
. 110536
33.3%
1 51081
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 169991
51.3%
. 110536
33.3%
1 51081
 
15.4%

Is_Not_In_School
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
92396 
1.0
18140 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 92396
83.6%
1.0 18140
 
16.4%

Length

2025-03-28T03:46:47.564060image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:47.645583image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 92396
83.6%
1.0 18140
 
16.4%

Most occurring characters

ValueCountFrequency (%)
0 202932
61.2%
. 110536
33.3%
1 18140
 
5.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 202932
61.2%
. 110536
33.3%
1 18140
 
5.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 202932
61.2%
. 110536
33.3%
1 18140
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 202932
61.2%
. 110536
33.3%
1 18140
 
5.5%

Is_Not_In_Techvoc
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
104727 
0.0
 
5809

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 104727
94.7%
0.0 5809
 
5.3%

Length

2025-03-28T03:46:47.735413image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:47.817115image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 104727
94.7%
0.0 5809
 
5.3%

Most occurring characters

ValueCountFrequency (%)
0 116345
35.1%
. 110536
33.3%
1 104727
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116345
35.1%
. 110536
33.3%
1 104727
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116345
35.1%
. 110536
33.3%
1 104727
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116345
35.1%
. 110536
33.3%
1 104727
31.6%

Is_Not_Working
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
70549 
1.0
39987 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 70549
63.8%
1.0 39987
36.2%

Length

2025-03-28T03:46:47.904584image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:47.989223image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 70549
63.8%
1.0 39987
36.2%

Most occurring characters

ValueCountFrequency (%)
0 181085
54.6%
. 110536
33.3%
1 39987
 
12.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 181085
54.6%
. 110536
33.3%
1 39987
 
12.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 181085
54.6%
. 110536
33.3%
1 39987
 
12.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 181085
54.6%
. 110536
33.3%
1 39987
 
12.1%

Has_Not_Wanted_More_Hours
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
96612 
0.0
13924 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 96612
87.4%
0.0 13924
 
12.6%

Length

2025-03-28T03:46:48.080935image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:48.163422image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 96612
87.4%
0.0 13924
 
12.6%

Most occurring characters

ValueCountFrequency (%)
0 124460
37.5%
. 110536
33.3%
1 96612
29.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 124460
37.5%
. 110536
33.3%
1 96612
29.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 124460
37.5%
. 110536
33.3%
1 96612
29.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 124460
37.5%
. 110536
33.3%
1 96612
29.1%

Has_Not_Looked_For_Work
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
103067 
0.0
 
7469

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 103067
93.2%
0.0 7469
 
6.8%

Length

2025-03-28T03:46:48.252350image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:48.332981image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 103067
93.2%
0.0 7469
 
6.8%

Most occurring characters

ValueCountFrequency (%)
0 118005
35.6%
. 110536
33.3%
1 103067
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 118005
35.6%
. 110536
33.3%
1 103067
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 118005
35.6%
. 110536
33.3%
1 103067
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 118005
35.6%
. 110536
33.3%
1 103067
31.1%

Is_Not_First_Time
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
109044 
0.0
 
1492

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 109044
98.7%
0.0 1492
 
1.3%

Length

2025-03-28T03:46:48.423310image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:48.505089image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 109044
98.7%
0.0 1492
 
1.3%

Most occurring characters

ValueCountFrequency (%)
0 112028
33.8%
. 110536
33.3%
1 109044
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112028
33.8%
. 110536
33.3%
1 109044
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112028
33.8%
. 110536
33.3%
1 109044
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112028
33.8%
. 110536
33.3%
1 109044
32.9%

No_Other_Job
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
104602 
0.0
 
5934

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 104602
94.6%
0.0 5934
 
5.4%

Length

2025-03-28T03:46:48.594394image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:48.682865image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 104602
94.6%
0.0 5934
 
5.4%

Most occurring characters

ValueCountFrequency (%)
0 116470
35.1%
. 110536
33.3%
1 104602
31.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 116470
35.1%
. 110536
33.3%
1 104602
31.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 116470
35.1%
. 110536
33.3%
1 104602
31.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 116470
35.1%
. 110536
33.3%
1 104602
31.5%

Has_Not_Looked_For_Work_Week
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
108378 
0.0
 
2158

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 108378
98.0%
0.0 2158
 
2.0%

Length

2025-03-28T03:46:48.774046image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:48.860080image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 108378
98.0%
0.0 2158
 
2.0%

Most occurring characters

ValueCountFrequency (%)
0 112694
34.0%
. 110536
33.3%
1 108378
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 112694
34.0%
. 110536
33.3%
1 108378
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 112694
34.0%
. 110536
33.3%
1 108378
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 112694
34.0%
. 110536
33.3%
1 108378
32.7%

Education_Business
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
105660 
1.0
 
4876

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 105660
95.6%
1.0 4876
 
4.4%

Length

2025-03-28T03:46:48.950422image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:49.033026image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 105660
95.6%
1.0 4876
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 216196
65.2%
. 110536
33.3%
1 4876
 
1.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 216196
65.2%
. 110536
33.3%
1 4876
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 216196
65.2%
. 110536
33.3%
1 4876
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 216196
65.2%
. 110536
33.3%
1 4876
 
1.5%

Education_Other
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
104112 
1.0
 
6424

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 104112
94.2%
1.0 6424
 
5.8%

Length

2025-03-28T03:46:49.122003image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:49.203039image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 104112
94.2%
1.0 6424
 
5.8%

Most occurring characters

ValueCountFrequency (%)
0 214648
64.7%
. 110536
33.3%
1 6424
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 214648
64.7%
. 110536
33.3%
1 6424
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 214648
64.7%
. 110536
33.3%
1 6424
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 214648
64.7%
. 110536
33.3%
1 6424
 
1.9%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
97146 
1.0
13390 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 97146
87.9%
1.0 13390
 
12.1%

Length

2025-03-28T03:46:49.294138image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:49.377245image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 97146
87.9%
1.0 13390
 
12.1%

Most occurring characters

ValueCountFrequency (%)
0 207682
62.6%
. 110536
33.3%
1 13390
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 207682
62.6%
. 110536
33.3%
1 13390
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 207682
62.6%
. 110536
33.3%
1 13390
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 207682
62.6%
. 110536
33.3%
1 13390
 
4.0%

Education_Primary
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
75601 
1.0
34935 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 75601
68.4%
1.0 34935
31.6%

Length

2025-03-28T03:46:49.469991image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:49.562379image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 75601
68.4%
1.0 34935
31.6%

Most occurring characters

ValueCountFrequency (%)
0 186137
56.1%
. 110536
33.3%
1 34935
 
10.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 186137
56.1%
. 110536
33.3%
1 34935
 
10.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 186137
56.1%
. 110536
33.3%
1 34935
 
10.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 186137
56.1%
. 110536
33.3%
1 34935
 
10.5%

Education_STEM
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
102176 
1.0
 
8360

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 102176
92.4%
1.0 8360
 
7.6%

Length

2025-03-28T03:46:49.659654image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:49.748133image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 102176
92.4%
1.0 8360
 
7.6%

Most occurring characters

ValueCountFrequency (%)
0 212712
64.1%
. 110536
33.3%
1 8360
 
2.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 212712
64.1%
. 110536
33.3%
1 8360
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 212712
64.1%
. 110536
33.3%
1 8360
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 212712
64.1%
. 110536
33.3%
1 8360
 
2.5%

Education_Secondary
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
68996 
1.0
41540 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 68996
62.4%
1.0 41540
37.6%

Length

2025-03-28T03:46:49.838292image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:49.921181image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 68996
62.4%
1.0 41540
37.6%

Most occurring characters

ValueCountFrequency (%)
0 179532
54.1%
. 110536
33.3%
1 41540
 
12.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 179532
54.1%
. 110536
33.3%
1 41540
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 179532
54.1%
. 110536
33.3%
1 41540
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 179532
54.1%
. 110536
33.3%
1 41540
 
12.5%

Work48H_Other
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
110509 
1.0
 
27

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 110509
> 99.9%
1.0 27
 
< 0.1%

Length

2025-03-28T03:46:50.012502image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:50.093363image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110509
> 99.9%
1.0 27
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
0 221045
66.7%
. 110536
33.3%
1 27
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 221045
66.7%
. 110536
33.3%
1 27
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 221045
66.7%
. 110536
33.3%
1 27
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 221045
66.7%
. 110536
33.3%
1 27
 
< 0.1%

Work48H_Passion
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
110373 
1.0
 
163

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 110373
99.9%
1.0 163
 
0.1%

Length

2025-03-28T03:46:50.183016image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:50.267723image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 110373
99.9%
1.0 163
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 220909
66.6%
. 110536
33.3%
1 163
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 220909
66.6%
. 110536
33.3%
1 163
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 220909
66.6%
. 110536
33.3%
1 163
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 220909
66.6%
. 110536
33.3%
1 163
 
< 0.1%

Work48H_Requirement
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
104227 
1.0
 
6309

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row1.0

Common Values

ValueCountFrequency (%)
0.0 104227
94.3%
1.0 6309
 
5.7%

Length

2025-03-28T03:46:50.355858image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:50.441484image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 104227
94.3%
1.0 6309
 
5.7%

Most occurring characters

ValueCountFrequency (%)
0 214763
64.8%
. 110536
33.3%
1 6309
 
1.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 214763
64.8%
. 110536
33.3%
1 6309
 
1.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 214763
64.8%
. 110536
33.3%
1 6309
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 214763
64.8%
. 110536
33.3%
1 6309
 
1.9%

Work48H_Want
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
103934 
0.0
 
6602

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row0.0

Common Values

ValueCountFrequency (%)
1.0 103934
94.0%
0.0 6602
 
6.0%

Length

2025-03-28T03:46:50.530719image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:50.612518image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 103934
94.0%
0.0 6602
 
6.0%

Most occurring characters

ValueCountFrequency (%)
0 117138
35.3%
. 110536
33.3%
1 103934
31.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 117138
35.3%
. 110536
33.3%
1 103934
31.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 117138
35.3%
. 110536
33.3%
1 103934
31.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 117138
35.3%
. 110536
33.3%
1 103934
31.3%

Employment_Status_NotInWorkForce
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
85375 
1.0
25161 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 85375
77.2%
1.0 25161
 
22.8%

Length

2025-03-28T03:46:50.700852image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:50.782032image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 85375
77.2%
1.0 25161
 
22.8%

Most occurring characters

ValueCountFrequency (%)
0 195911
59.1%
. 110536
33.3%
1 25161
 
7.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 195911
59.1%
. 110536
33.3%
1 25161
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 195911
59.1%
. 110536
33.3%
1 25161
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 195911
59.1%
. 110536
33.3%
1 25161
 
7.6%

Employment_Status_Unemployed
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
106505 
1.0
 
4031

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 106505
96.4%
1.0 4031
 
3.6%

Length

2025-03-28T03:46:50.872195image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:50.957122image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 106505
96.4%
1.0 4031
 
3.6%

Most occurring characters

ValueCountFrequency (%)
0 217041
65.5%
. 110536
33.3%
1 4031
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 217041
65.5%
. 110536
33.3%
1 4031
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 217041
65.5%
. 110536
33.3%
1 4031
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 217041
65.5%
. 110536
33.3%
1 4031
 
1.2%

NatureOfEmployment_Permanent
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
1.0
93166 
0.0
17370 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row0.0
3rd row0.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 93166
84.3%
0.0 17370
 
15.7%

Length

2025-03-28T03:46:51.047175image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:51.131134image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
1.0 93166
84.3%
0.0 17370
 
15.7%

Most occurring characters

ValueCountFrequency (%)
0 127906
38.6%
. 110536
33.3%
1 93166
28.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 127906
38.6%
. 110536
33.3%
1 93166
28.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 127906
38.6%
. 110536
33.3%
1 93166
28.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 127906
38.6%
. 110536
33.3%
1 93166
28.1%

NatureOfEmployment_Short-Term
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.7 MiB
0.0
95699 
1.0
14837 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters331608
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row1.0
3rd row1.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 95699
86.6%
1.0 14837
 
13.4%

Length

2025-03-28T03:46:51.220510image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-28T03:46:51.302687image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0 95699
86.6%
1.0 14837
 
13.4%

Most occurring characters

ValueCountFrequency (%)
0 206235
62.2%
. 110536
33.3%
1 14837
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 206235
62.2%
. 110536
33.3%
1 14837
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 206235
62.2%
. 110536
33.3%
1 14837
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 331608
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 206235
62.2%
. 110536
33.3%
1 14837
 
4.5%

Hours_Work_Total
Real number (ℝ)

High correlation 

Distinct110
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean44.03578
Minimum0
Maximum112
Zeros1058
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:51.407048image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile12
Q140
median48
Q348
95-th percentile70
Maximum112
Range112
Interquartile range (IQR)8

Descriptive statistics

Standard deviation15.716474
Coefficient of variation (CV)0.35690237
Kurtosis2.0454568
Mean44.03578
Median Absolute Deviation (MAD)0
Skewness-0.21224312
Sum4867539
Variance247.00757
MonotonicityNot monotonic
2025-03-28T03:46:51.771405image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
48 57662
52.2%
40 8447
 
7.6%
56 3394
 
3.1%
24 2706
 
2.4%
60 2688
 
2.4%
42 2232
 
2.0%
70 2129
 
1.9%
36 2029
 
1.8%
30 1872
 
1.7%
72 1569
 
1.4%
Other values (100) 25808
23.3%
ValueCountFrequency (%)
0 1058
1.0%
1 68
 
0.1%
2 162
 
0.1%
3 170
 
0.2%
4 366
 
0.3%
5 128
 
0.1%
6 562
0.5%
7 443
0.4%
8 1016
0.9%
9 351
 
0.3%
ValueCountFrequency (%)
112 159
0.1%
110 2
 
< 0.1%
109 1
 
< 0.1%
108 3
 
< 0.1%
106 3
 
< 0.1%
105 168
0.2%
104 1
 
< 0.1%
103 1
 
< 0.1%
102 7
 
< 0.1%
101 1
 
< 0.1%

Marital_Status
Real number (ℝ)

High correlation  Zeros 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76660997
Minimum0
Maximum5
Zeros37943
Zeros (%)34.3%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:51.872290image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.672129
Coefficient of variation (CV)0.87675484
Kurtosis1.9441069
Mean0.76660997
Median Absolute Deviation (MAD)0
Skewness0.88022459
Sum84738
Variance0.4517574
MonotonicityNot monotonic
2025-03-28T03:46:51.965619image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
1 63205
57.2%
0 37943
34.3%
2 6757
 
6.1%
3 2556
 
2.3%
5 51
 
< 0.1%
4 24
 
< 0.1%
ValueCountFrequency (%)
0 37943
34.3%
1 63205
57.2%
2 6757
 
6.1%
3 2556
 
2.3%
4 24
 
< 0.1%
5 51
 
< 0.1%
ValueCountFrequency (%)
5 51
 
< 0.1%
4 24
 
< 0.1%
3 2556
 
2.3%
2 6757
 
6.1%
1 63205
57.2%
0 37943
34.3%

Working_Hours_Per_Week
Real number (ℝ)

High correlation 

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6651589
Minimum0
Maximum15
Zeros901
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:52.056802image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q17
median7
Q37
95-th percentile10
Maximum15
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0153993
Coefficient of variation (CV)0.30237828
Kurtosis2.9987284
Mean6.6651589
Median Absolute Deviation (MAD)0
Skewness-0.42801756
Sum736740
Variance4.0618343
MonotonicityNot monotonic
2025-03-28T03:46:52.158882image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
7 74879
67.7%
5 5914
 
5.4%
9 5465
 
4.9%
3 4668
 
4.2%
4 3896
 
3.5%
11 3627
 
3.3%
8 2410
 
2.2%
2 2346
 
2.1%
6 2279
 
2.1%
1 2160
 
2.0%
Other values (6) 2892
 
2.6%
ValueCountFrequency (%)
0 901
 
0.8%
1 2160
 
2.0%
2 2346
 
2.1%
3 4668
 
4.2%
4 3896
 
3.5%
5 5914
 
5.4%
6 2279
 
2.1%
7 74879
67.7%
8 2410
 
2.2%
9 5465
 
4.9%
ValueCountFrequency (%)
15 295
 
0.3%
14 277
 
0.3%
13 404
 
0.4%
12 418
 
0.4%
11 3627
 
3.3%
10 597
 
0.5%
9 5465
 
4.9%
8 2410
 
2.2%
7 74879
67.7%
6 2279
 
2.1%

Class_Of_Worker
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7552381
Minimum0
Maximum6
Zeros3296
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:52.254479image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q33
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.3806427
Coefficient of variation (CV)0.78658427
Kurtosis2.5499841
Mean1.7552381
Median Absolute Deviation (MAD)0
Skewness1.7394026
Sum194017
Variance1.9061742
MonotonicityNot monotonic
2025-03-28T03:46:52.346226image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
1 71592
64.8%
3 20673
 
18.7%
6 6289
 
5.7%
2 6144
 
5.6%
0 3296
 
3.0%
4 2326
 
2.1%
5 216
 
0.2%
ValueCountFrequency (%)
0 3296
 
3.0%
1 71592
64.8%
2 6144
 
5.6%
3 20673
 
18.7%
4 2326
 
2.1%
5 216
 
0.2%
6 6289
 
5.7%
ValueCountFrequency (%)
6 6289
 
5.7%
5 216
 
0.2%
4 2326
 
2.1%
3 20673
 
18.7%
2 6144
 
5.6%
1 71592
64.8%
0 3296
 
3.0%

Payment_Basis
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9756278
Minimum0
Maximum7
Zeros257
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1.7 MiB
2025-03-28T03:46:52.438791image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median4
Q34
95-th percentile6
Maximum7
Range7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.84721113
Coefficient of variation (CV)0.21310122
Kurtosis7.5648058
Mean3.9756278
Median Absolute Deviation (MAD)0
Skewness1.4337035
Sum439450
Variance0.71776669
MonotonicityNot monotonic
2025-03-28T03:46:52.530185image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4 86398
78.2%
3 16263
 
14.7%
7 5307
 
4.8%
1 837
 
0.8%
5 721
 
0.7%
6 493
 
0.4%
2 260
 
0.2%
0 257
 
0.2%
ValueCountFrequency (%)
0 257
 
0.2%
1 837
 
0.8%
2 260
 
0.2%
3 16263
 
14.7%
4 86398
78.2%
5 721
 
0.7%
6 493
 
0.4%
7 5307
 
4.8%
ValueCountFrequency (%)
7 5307
 
4.8%
6 493
 
0.4%
5 721
 
0.7%
4 86398
78.2%
3 16263
 
14.7%
2 260
 
0.2%
1 837
 
0.8%
0 257
 
0.2%

Interactions

2025-03-28T03:46:44.614868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:39.726826image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.411204image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.092554image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.788264image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.485079image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.179886image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.906104image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.699178image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:39.810959image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.490761image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.175773image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.872837image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.566948image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.268268image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.993222image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.786933image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:39.892176image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.573218image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.260819image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.954553image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.648354image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.356724image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.076864image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.875389image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:39.973720image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.655367image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.342917image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.039527image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.735016image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.446744image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.163799image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:45.157520image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.060096image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.739131image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.428807image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.125363image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.821306image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.540104image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.249484image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:45.242547image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.145091image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.821102image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.513771image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.211118image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.904093image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.626916image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.339705image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:45.347163image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.236721image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.911553image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.606812image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.307379image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.000498image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.719690image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.433894image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:45.453061image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.322082image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:40.999280image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:41.695474image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:42.395097image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.086838image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:43.810864image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
2025-03-28T03:46:44.522868image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/

Correlations

2025-03-28T03:46:52.634409image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
AgeClass_Of_WorkerEducation_BusinessEducation_OtherEducation_Post-SecondaryEducation_PrimaryEducation_STEMEducation_SecondaryEmployment_Status_NotInWorkForceEmployment_Status_UnemployedHas_Not_Looked_For_WorkHas_Not_Looked_For_Work_WeekHas_Not_Wanted_More_HoursHours_Work_TotalHousehold_SizeIs_FemaleIs_Not_First_TimeIs_Not_In_SchoolIs_Not_In_TechvocIs_Not_WorkingIs_RuralMarital_StatusNatureOfEmployment_PermanentNatureOfEmployment_Short-TermNo_Other_JobPayment_BasisRelationshipWork48H_OtherWork48H_PassionWork48H_RequirementWork48H_WantWorking_Hours_Per_Week
Age1.0000.2090.0910.1150.1100.3130.1490.1370.3210.1360.1010.1250.142-0.087-0.2280.0690.1560.5730.0910.3760.0610.6590.1550.1540.1260.063-0.5330.0000.0220.0880.087-0.086
Class_Of_Worker0.2091.0000.0910.2400.0600.1670.1230.1110.4000.1430.1300.1040.184-0.283-0.0540.1730.0610.1740.0350.5270.1910.1580.1290.1350.1970.180-0.2230.0310.0390.2920.285-0.313
Education_Business0.0910.0911.0000.0530.0800.1460.0610.1670.0020.0120.0330.0150.0380.0860.0360.0950.0030.0190.0000.0110.0960.0240.0440.0340.0280.0600.0370.0000.0000.0110.0110.063
Education_Other0.1150.2400.0531.0000.0920.1690.0710.1930.0020.0410.0290.0480.0340.0830.0280.0660.0130.0140.0130.0240.0190.0240.0480.0370.0230.0720.0500.0000.0010.0140.0140.077
Education_Post-Secondary0.1100.0600.0800.0921.0000.2520.1060.2880.0920.0170.0260.0150.0280.0550.0160.0240.0040.0250.0370.0540.0740.0520.0150.0000.0270.0430.0600.0000.0030.0020.0020.056
Education_Primary0.3130.1670.1460.1690.2521.0000.1940.5270.0110.0640.0470.0640.0390.1130.0520.0550.0470.0360.1410.0380.1870.1270.0050.0200.0660.0410.1520.0000.0030.0450.0440.137
Education_STEM0.1490.1230.0610.0710.1060.1941.0000.2220.0290.0300.0300.0320.0310.0900.0420.0040.0190.0030.0550.0050.0850.0560.0440.0330.0250.0540.0810.0000.0020.0100.0090.083
Education_Secondary0.1370.1110.1670.1930.2880.5270.2221.0000.0340.0080.0190.0000.0340.0980.0270.0370.0210.0510.0740.0740.0260.0540.0750.0740.0080.1140.0640.0000.0050.0650.0630.092
Employment_Status_NotInWorkForce0.3210.4000.0020.0020.0920.0110.0290.0341.0000.1060.1460.0360.2060.4570.0490.2480.0630.0050.0110.7210.0250.1700.2340.2140.1290.2870.1760.0070.0200.1330.1370.375
Employment_Status_Unemployed0.1360.1430.0120.0410.0170.0640.0300.0080.1061.0000.0520.6340.0740.1640.0230.0330.0220.1310.0290.2580.0300.1210.0840.0760.0460.1030.1400.0000.0060.0480.0490.134
Has_Not_Looked_For_Work0.1010.1300.0330.0290.0260.0470.0300.0190.1460.0521.0000.0380.5790.2140.0360.1020.0210.0130.0130.1950.1040.0730.1480.1050.3390.1510.0920.0000.0000.0290.0280.177
Has_Not_Looked_For_Work_Week0.1250.1040.0150.0480.0150.0640.0320.0000.0360.6340.0381.0000.0530.1180.0200.0100.0160.1140.0320.1870.0380.1000.0610.0550.0330.0740.1140.0000.0030.0340.0350.097
Has_Not_Wanted_More_Hours0.1420.1840.0380.0340.0280.0390.0310.0340.2060.0740.5790.0531.0000.2860.0460.1170.0180.0270.0190.2750.1040.1060.1740.1350.2920.1840.1270.0040.0000.0340.0320.223
Hours_Work_Total-0.087-0.2830.0860.0830.0550.1130.0900.0980.4570.1640.2140.1180.2861.0000.0290.1090.0430.0750.0210.6000.175-0.0460.2360.2040.1380.0100.0890.0350.0590.4690.4750.719
Household_Size-0.228-0.0540.0360.0280.0160.0520.0420.0270.0490.0230.0360.0200.0460.0291.0000.0110.0170.1060.0330.1300.039-0.1710.0300.0280.0380.0110.3250.0000.0040.0180.0200.016
Is_Female0.0690.1730.0950.0660.0240.0550.0040.0370.2480.0330.1020.0100.1170.1090.0111.0000.0000.0220.0320.2120.0300.1620.0980.0700.0930.2160.1030.0040.0090.0110.0140.090
Is_Not_First_Time0.1560.0610.0030.0130.0040.0470.0190.0210.0630.0220.0210.0160.0180.0430.0170.0001.0000.1430.0000.0860.0230.1230.1230.1290.0170.0550.1190.0000.0000.0440.0440.028
Is_Not_In_School0.5730.1740.0190.0140.0250.0360.0030.0510.0050.1310.0130.1140.0270.0750.1060.0220.1431.0000.0000.0400.0000.3760.1160.1180.0610.0770.4070.0000.0100.0360.0330.076
Is_Not_In_Techvoc0.0910.0350.0000.0130.0370.1410.0550.0740.0110.0290.0130.0320.0190.0210.0330.0320.0000.0001.0000.0110.0370.0330.0000.0080.0050.0260.0420.0000.0060.0040.0020.027
Is_Not_Working0.3760.5270.0110.0240.0540.0380.0050.0740.7210.2580.1950.1870.2750.6000.1300.2120.0860.0400.0111.0000.0400.2270.3170.2890.1780.3850.3050.0110.0290.1850.1900.500
Is_Rural0.0610.1910.0960.0190.0740.1870.0850.0260.0250.0300.1040.0380.1040.1750.0390.0300.0230.0000.0370.0401.0000.0490.0730.0500.1250.0350.0900.0000.0080.0660.0630.194
Marital_Status0.6590.1580.0240.0240.0520.1270.0560.0540.1700.1210.0730.1000.106-0.046-0.1710.1620.1230.3760.0330.2270.0491.0000.0680.0760.1110.032-0.5440.0000.0090.0290.028-0.045
NatureOfEmployment_Permanent0.1550.1290.0440.0480.0150.0050.0440.0750.2340.0840.1480.0610.1740.2360.0300.0980.1230.1160.0000.3170.0730.0681.0000.9120.0410.3990.0980.0000.0120.0400.0380.123
NatureOfEmployment_Short-Term0.1540.1350.0340.0370.0000.0200.0330.0740.2140.0760.1050.0550.1350.2040.0280.0700.1290.1180.0080.2890.0500.0760.9121.0000.0250.2900.1010.0010.0100.0510.0490.130
No_Other_Job0.1260.1970.0280.0230.0270.0660.0250.0080.1290.0460.3390.0330.2920.1380.0380.0930.0170.0610.0050.1780.1250.1110.0410.0251.0000.0820.1360.0100.0150.0310.0260.194
Payment_Basis0.0630.1800.0600.0720.0430.0410.0540.1140.2870.1030.1510.0740.1840.0100.0110.2160.0550.0770.0260.3850.0350.0320.3990.2900.0821.0000.0120.0000.0120.0870.085-0.044
Relationship-0.533-0.2230.0370.0500.0600.1520.0810.0640.1760.1400.0920.1140.1270.0890.3250.1030.1190.4070.0420.3050.090-0.5440.0980.1010.1360.0121.0000.0030.0140.2510.2460.078
Work48H_Other0.0000.0310.0000.0000.0000.0000.0000.0000.0070.0000.0000.0000.0040.0350.0000.0040.0000.0000.0000.0110.0000.0000.0000.0010.0100.0000.0031.0000.0000.0000.0610.031
Work48H_Passion0.0220.0390.0000.0010.0030.0030.0020.0050.0200.0060.0000.0030.0000.0590.0040.0090.0000.0100.0060.0290.0080.0090.0120.0100.0150.0120.0140.0001.0000.0080.1520.067
Work48H_Requirement0.0880.2920.0110.0140.0020.0450.0100.0650.1330.0480.0290.0340.0340.4690.0180.0110.0440.0360.0040.1850.0660.0290.0400.0510.0310.0870.2510.0000.0081.0000.9760.473
Work48H_Want0.0870.2850.0110.0140.0020.0440.0090.0630.1370.0490.0280.0350.0320.4750.0200.0140.0440.0330.0020.1900.0630.0280.0380.0490.0260.0850.2460.0610.1520.9761.0000.478
Working_Hours_Per_Week-0.086-0.3130.0630.0770.0560.1370.0830.0920.3750.1340.1770.0970.2230.7190.0160.0900.0280.0760.0270.5000.194-0.0450.1230.1300.194-0.0440.0780.0310.0670.4730.4781.000

Missing values

2025-03-28T03:46:45.613937image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-28T03:46:46.102307image/svg+xmlMatplotlib v3.9.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Household_SizeRelationshipAgeIs_RuralIs_FemaleIs_Not_In_SchoolIs_Not_In_TechvocIs_Not_WorkingHas_Not_Wanted_More_HoursHas_Not_Looked_For_WorkIs_Not_First_TimeNo_Other_JobHas_Not_Looked_For_Work_WeekEducation_BusinessEducation_OtherEducation_Post-SecondaryEducation_PrimaryEducation_STEMEducation_SecondaryWork48H_OtherWork48H_PassionWork48H_RequirementWork48H_WantEmployment_Status_NotInWorkForceEmployment_Status_UnemployedNatureOfEmployment_PermanentNatureOfEmployment_Short-TermHours_Work_TotalMarital_StatusWorking_Hours_Per_WeekClass_Of_WorkerPayment_Basis
031491.00.00.01.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0321734
132611.01.00.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.00.01.081364
233191.00.01.01.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.00.01.0340713
341481.00.00.01.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0331334
442411.01.00.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.01.00.00.00.01.00.07211104
543201.00.01.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.00.01.0480713
643151.01.00.01.01.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.01.00.01.00.0480714
741591.00.00.01.00.00.00.01.00.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0321334
842611.01.00.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0101727
946111.01.00.01.01.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.0480714
Household_SizeRelationshipAgeIs_RuralIs_FemaleIs_Not_In_SchoolIs_Not_In_TechvocIs_Not_WorkingHas_Not_Wanted_More_HoursHas_Not_Looked_For_WorkIs_Not_First_TimeNo_Other_JobHas_Not_Looked_For_Work_WeekEducation_BusinessEducation_OtherEducation_Post-SecondaryEducation_PrimaryEducation_STEMEducation_SecondaryWork48H_OtherWork48H_PassionWork48H_RequirementWork48H_WantEmployment_Status_NotInWorkForceEmployment_Status_UnemployedNatureOfEmployment_PermanentNatureOfEmployment_Short-TermHours_Work_TotalMarital_StatusWorking_Hours_Per_WeekClass_Of_WorkerPayment_Basis
18084243271.00.00.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.00.01.01.00.01.00.0480714
18084461551.00.00.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0481714
18084663301.01.00.01.01.01.01.01.01.01.01.00.00.00.00.00.00.00.00.01.01.00.01.00.0480714
18084763271.01.00.01.00.01.01.01.01.01.00.00.00.00.01.00.00.00.00.01.00.00.01.00.0480724
18084863251.00.00.01.00.01.01.01.01.01.01.00.00.00.00.00.00.00.00.01.00.00.01.00.0450724
18085071341.00.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.0401434
18085172321.01.00.01.00.00.00.01.00.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.0481334
18085751291.00.00.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0401744
18085852291.01.00.01.01.01.01.01.01.01.00.00.01.00.00.00.00.00.00.01.01.00.01.00.0481714
18086158181.00.01.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.0280334

Duplicate rows

Most frequently occurring

Household_SizeRelationshipAgeIs_RuralIs_FemaleIs_Not_In_SchoolIs_Not_In_TechvocIs_Not_WorkingHas_Not_Wanted_More_HoursHas_Not_Looked_For_WorkIs_Not_First_TimeNo_Other_JobHas_Not_Looked_For_Work_WeekEducation_BusinessEducation_OtherEducation_Post-SecondaryEducation_PrimaryEducation_STEMEducation_SecondaryWork48H_OtherWork48H_PassionWork48H_RequirementWork48H_WantEmployment_Status_NotInWorkForceEmployment_Status_UnemployedNatureOfEmployment_PermanentNatureOfEmployment_Short-TermHours_Work_TotalMarital_StatusWorking_Hours_Per_WeekClass_Of_WorkerPayment_Basis# duplicates
402153101.00.00.01.00.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.048071411
520963121.00.00.01.00.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.048071410
272943111.01.00.01.01.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.04807148
273843121.00.00.01.01.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.04807148
379752401.01.00.01.01.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.01.00.01.00.04817148
403153111.00.00.01.00.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.04807148
403453111.01.00.01.00.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.04807148
405253130.00.00.01.01.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.04807148
405853131.00.00.01.00.01.01.01.01.01.00.00.00.00.00.01.00.00.00.01.00.00.01.00.04807148
522563131.00.00.01.00.01.01.01.01.01.00.00.00.01.00.00.00.00.00.01.00.00.01.00.04807148